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Bioengineering
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Next-Generation Biosignal Engineering: AI-Driven Diagnostics, Prosthetic Interfaces and Multimodal Physiological...
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Next-Generation Biosignal Engineering: AI-Driven Diagnostics, Prosthetic Interfaces and Multimodal Physiological Sensing

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 2040

Special Issue Editors

Dr. Ejay Nsugbe

E-Mail Website
Guest Editor
Nsugbe Research Labs, Swindon SN1 3LG, UK
Interests: signal processing; machine learning; clinical medicine; cybernetics; public health; intelligent systems
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Mesin

E-Mail Website
Guest Editor
Department of Electronics and Telecommunications, Polytechnic University of Turin, 10129 Turin, Italy
Interests: biomedical signal and image processing and classification; biophysical modelling; clinical studies; mathematical biology and physiology; noninvasive monitoring of the volemic status of patients; nonlinear biomedical signal processing; optimal non-uniform down-sampling; systems for human–machine interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in artificial intelligence, multimodal signal processing, and bio-sensor design have created a new frontier in biomedical engineering, enabling unprecedented resolution, interpretability, and clinical utility in physiological monitoring. This Special Issue aims to showcase cutting-edge research at the intersection of computational biosignal analytics, wearable and implantable sensor technologies, and intelligent diagnostic systems. Of particular interest are innovations that integrate machine learning with advanced signal-processing pipelines—such as sparse-representation methods, adaptive feature extraction, and low-signal-density learning—to enhance early disease detection, neuro-musculoskeletal function assessment, and next-generation prosthetic and rehabilitation interfaces. Contributions addressing translational pathways, validation frameworks, and real-world deployment in clinical or public-health contexts are strongly encouraged. By gathering perspectives from engineering, medicine, neuroscience, and data science, this Special Issue seeks to map the emerging landscape of AI-enabled bioengineering and its potential to reshape personalised diagnostics, population-health surveillance, and patient-centred care.

Dr. Ejay Nsugbe
Dr. Luca Mesin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI-driven diagnostics
  • multimodal biosignal processing
  • wearable and implantable sensors
  • prosthetic and rehabilitation interfaces
  • sparse-signal and low-density learning (LSDL-aligned)
  • biomedical machine learning
  • physiological sensing and monitoring
  • translational bioengineering
  • computational neuro-musculoskeletal assessment
  • personalized and population-health technologies

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Published Papers (2 papers)

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Research

19 pages, 4117 KB  
Article
Automatic Personal Identification Using a Single MRI Slice
by Andreas Heinrich
Bioengineering 2026, 13(5), 494; https://doi.org/10.3390/bioengineering13050494 - 24 Apr 2026
Viewed by 1024
Abstract
Identification of unknown individuals is challenging, and radiological imaging databases provide rich anatomical information for automated recognition. This study evaluated whether a single routine magnetic resonance imaging (MRI) slice contains sufficient person-specific features to identify individuals in large databases. It analyzed 11,078 head [...] Read more.
Identification of unknown individuals is challenging, and radiological imaging databases provide rich anatomical information for automated recognition. This study evaluated whether a single routine magnetic resonance imaging (MRI) slice contains sufficient person-specific features to identify individuals in large databases. It analyzed 11,078 head MRI examinations from 5770 individuals (age 52 ± 18 years, 2714 men) acquired between 2002 and 2025. For identification, 112 individuals were randomly selected across eight 10-year age groups, and one slice from four anatomical regions was extracted. The remaining 10,966 MRI examinations with 247,804 slices formed the reference database. Distinctive anatomical features were automatically extracted using computer vision (CV), and the identification rate was evaluated by rank. Using a single MRI slice, the identification rate at rank 1 reached 96% (107/112) for the best-performing region, the maxillary sinus, among 5770 potential identities. Across all regions, the rank 1 identification rate ranged from 91% to 96%; combining them increased rank 1 and 10 identification rates to 98% (110/112) and 99% (111/112). Identification rate remained stable over several years, with only two cases showing reduced rank 1 performance, likely due to age-related morphological changes. A single MRI slice contains stable, individualized features sufficient for reliable identification in large databases, supporting automated CV-based personal identification across years. Full article
(This article belongs to the Special Issue Next-Generation Biosignal Engineering: AI-Driven Diagnostics, Prosthetic Interfaces and Multimodal Physiological Sensing)
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19 pages, 3413 KB  
Article
AI-Based Angle Map Analysis of Facial Asymmetry in Peripheral Facial Palsy
by Andreas Heinrich, Gerd Fabian Volk, Christian Dobel and Orlando Guntinas-Lichius
Bioengineering 2026, 13(4), 426; https://doi.org/10.3390/bioengineering13040426 - 6 Apr 2026
Viewed by 729
Abstract
Peripheral facial palsy (PFP) causes pronounced facial asymmetry and functional impairment, highlighting the need for reliable, objective assessment. This study presents a novel, fully automated, reference-free method for quantifying facial symmetry using artificial intelligence (AI)-based facial landmark detection. A total of 405 datasets [...] Read more.
Peripheral facial palsy (PFP) causes pronounced facial asymmetry and functional impairment, highlighting the need for reliable, objective assessment. This study presents a novel, fully automated, reference-free method for quantifying facial symmetry using artificial intelligence (AI)-based facial landmark detection. A total of 405 datasets from 198 PFP patients were analyzed, each including nine standardized facial expressions covering both resting and dynamic movements. AI detected 478 landmarks per image, from which 225 paired landmarks were used to compute local asymmetry angles. Systematic evaluation identified 91 highly informative landmark pairs, primarily around the eyes, nose and mouth, which simplified the analysis and enhanced discriminatory power, while also enabling region-specific assessment of asymmetry. Statistical evaluation included Kruskal–Wallis H-tests across clinical scores and Spearman correlations, showing moderate to strong associations (0.32–0.73, p < 0.001). The fully automated pipeline produced reproducible results and demonstrated robustness to head rotation. Intuitive full-face angle maps allowed direct assessment of asymmetry without a reference image. This AI-driven approach provides a robust, objective, and visually interpretable framework for clinical monitoring, severity classification, and treatment evaluation in PFP, combining quantitative precision with practical applicability. Full article
(This article belongs to the Special Issue Next-Generation Biosignal Engineering: AI-Driven Diagnostics, Prosthetic Interfaces and Multimodal Physiological Sensing)
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